CN113674135A - Calculation method for realizing CALPUFF high performance based on workflow - Google Patents

Abstract

A calculation method for realizing CALPUFF high performance based on workflow relates to the technical field of atmospheric environment and computer processing, and solves the problems that the existing meteorological model has poor processing capacity for abnormal conditions, the calculation cannot support concurrency, the calculation efficiency is low, and the analysis processing capacity for calculation results is insufficient; and the method can not support the calculation of complex conditions of multiple models, and can only solve the problems of singleness and the like by a single script. The method is realized based on an atmospheric model computing service system, and one computing task is divided into a plurality of task nodes according to a workflow mode and is completed one by one. The atmospheric model calculation service system adopts a numerical simulation method, analyzes the atmospheric pollution causes of different climatic conditions and different times in the range of a numerical model, judges the contribution rate of each pollution source, each pollution source and each grid pollution source which influence the atmospheric pollution, and provides a decision support system for atmospheric pollution prevention and control.

Description

Calculation method for realizing CALPUFF high performance based on workflow

Technical Field

The invention relates to the technical field of atmospheric environment and computer processing, in particular to a method for realizing CALPUFF high-performance calculation through workflow and platformization.

Background

The existing meteorological models with different scales are provided with WRF, CALMET and CALPUFF from large scale to small scale in sequence. If a precise small-scale CALPUFF calculation result is required, the CALPUFF model can be calculated by firstly calculating a WRF model and then calculating a CALMET model, and then the calculation result is used as pre-calculation data of the CALPUFF model to perform CALPUFF model calculation.

At present, most of mode calculation is performed manually, so that the workload is large, the calculation efficiency is low, and errors are easy to generate. A small part of the data is connected in series with the whole process through a simpler script tool, but the processing capacity of the method for various abnormal conditions is poor, the calculation cannot support concurrency, the calculation efficiency is low, and the analysis processing capacity for the calculation result is also insufficient; and the method cannot support the calculation of complex conditions of multiple models, and only a single script can solve a single problem.

In the demand scene of the current market, a solution capable of stably and efficiently processing complex computing flow is needed, and a solution capable of having strong integration capability of various data sources and data analysis processing capability is needed. The core function of the atmospheric model computing service platform is that through an advanced system architecture, complex computing flow can be processed stably and efficiently, various data sources are integrated, and the automation level of the complex computing flow and the processing and analyzing capability of a final result can be greatly improved. Meanwhile, the atmosphere model computing service platform can be opened to the outside in a cloud service mode, and high-quality service is brought to customers.

Disclosure of Invention

The method aims to solve the problems that the existing meteorological model has poor processing capacity for abnormal conditions, the calculation cannot support concurrency, the calculation efficiency is low, and the analysis processing capacity for calculation results is insufficient; and the method can not support the calculation of multi-mode complex conditions, and can only solve the problems of singleness and the like by a single script. A calculation method for realizing CALPUFF high performance based on workflow is provided.

The calculation method for realizing the CALPUFF high performance based on the workflow is realized based on an atmosphere model calculation service system, wherein the atmosphere model calculation service system comprises an atmosphere model management system, an atmosphere model calculation system and a task scheduling management system;

the third-party system interacts with the whole atmosphere model calculation service system through the atmosphere model management system;

the atmosphere model management system is used for facing the use of a user and interacting with a third-party system; the system is used for realizing the functions of management of a calculation model, case management, statistical analysis of model data, management of a calculation service interface and system management;

the atmosphere model calculation system is responsible for completing the calculation process of the model and the subsequent processing of the calculation result; the method specifically comprises WRF model calculation, CALMET model calculation, CALPUFF model calculation and CALPUT model calculation;

the atmospheric model computing system divides a computing task into a plurality of task nodes according to the mode of a workflow and completes the task nodes one by one; the task nodes comprise WRF calculating sub-nodes, CALMET calculating sub-nodes, CALPUFF calculating sub-nodes, data processing sub-nodes and picture rendering sub-nodes;

the WRF calculation sub-node is used for completing CALRF model calculation; the task of the CALMET calculation sub-node is used for completing CALMET model calculation; the task of the CALPUFF calculation child node is used for completing CALPUFF model calculation; the task of the data processing child node is used for analyzing and storing data of the CALPUFF model calculation result; the task of the picture rendering child node is mainly to perform picture rendering on data of a calpuff result;

the task scheduling management system is responsible for scheduling and managing each task in the atmosphere model computing system; the task scheduling management system groups the tasks, realizes the operations of suspending, immediately executing, executing according to the time rule and resuming the execution, and is also used for checking the logs of the task execution.

The invention has the beneficial effects that:

the method integrates the complicated calculation processes: and integrating complex computing processes through a workflow engine. Therefore, the computing links can be coupled efficiently, and the computing links can be flexibly integrated to deal with the change of the computing process in different computing scenes.

And (3) integrating complex data: various data can be used and generated in the whole complex computing process, and the independent data modules can effectively manage and use the complex data sources so as to provide strong data support for complex computing scenes.

Data analysis and processing: the processing and analysis of the data have two levels, wherein the first level is the analysis and processing of the CALPUFF calculation result; the second layer is to combine the calculation result of CALPUFF with various service data to perform secondary analysis processing. Through different layers and integration of various service data sources, the data analysis requirement of more latitudes can be met.

Cloud service sharing: through a cloud service mode, the cloud platform can be integrated with a third-party platform, and convenient service is provided for third-party users. The service can have two latitudes, one functional, and can meet the service requirements of different users; the second latitude is performance, and the concurrent advantage of the platform can provide efficient calculation for users, so that the time cost of the users is greatly saved.

The atmospheric model calculation service system adopts a numerical simulation method to analyze the atmospheric pollution causes of different climatic conditions and different times in the range of a numerical model, judges the contribution rates of various pollution sources, various pollution sources and various grid pollution sources which influence the atmospheric pollution and provides decision support for atmospheric pollution prevention and treatment.

The atmospheric model calculation service system is composed of a plurality of subsystems, each subsystem needs to be independently deployed and shares different tasks, the functions of model management, model calculation, result post-processing, data analysis and the like are completed through mutual cooperation, and secondary development interfaces and services can be provided in various modes.

The atmosphere model computing service system can run on various operating systems, such as Linux and Windows systems, the platform adopts an advanced technical framework, concurrent computing is supported, mass data processing is carried out, and various types of pictures are rendered in real time.

Drawings

FIG. 1 is a general business architecture diagram of an atmospheric model computing service platform;

fig. 2 is a network architecture diagram of each subsystem of the atmospheric model computing service system and an external system in the computing method for implementing CALPUFF high performance based on workflow according to the present invention;

FIG. 3 is an overall technical architecture diagram of an atmosphere model computing service system;

FIG. 4 is a block diagram of MVC pattern;

FIG. 5 is a schematic diagram of the operational mode of the WRF model;

FIG. 6 is a block diagram of operational mode parameters of the WRF model;

FIG. 7 is a graph of the effect of simulations using the WRF model;

FIG. 8 is a graph of the effect of simulations using the CALMET model;

FIG. 9 is a computational schematic of the CALMET model.

Detailed Description

In the first embodiment, the calculation method for realizing CALPUFF high performance based on a workflow is described with reference to fig. 1 to 9, and the calculation method is based on an atmosphere model calculation service system (platform) and is composed of three systems, specifically including an atmosphere model management system, an atmosphere model calculation system and a task scheduling management system.

The third-party system interacts with the whole atmosphere model calculation service system through the atmosphere model management system;

the atmosphere model management system is used for facing the use of a user and interacting with a third-party system; the system is used for realizing the functions of management of a calculation model, case management, statistical analysis of model data, management of a calculation service interface and system management;

the atmosphere model calculation system is responsible for completing the calculation process of the model and the subsequent processing of the calculation result; the method specifically comprises WRF model calculation, CALMET model calculation, CALPUFF model calculation, CALPUT model calculation and subsequent processing on model results; the subsequent processing comprises picture rendering and contribution rate analysis.

After the case calculation is finished, the calculation result can be rendered into various pictures. The user can preset rendering schemes of various pictures so as to refer to the schemes for picture rendering in the calculation process. And carrying out various data statistical analyses on the data of the calculation result.

The atmospheric model computing system divides a computing task into a plurality of task nodes according to the mode of a workflow and completes the task nodes one by one; the task nodes comprise WRF calculation sub-nodes, CALMET calculation sub-nodes, CALPUFF calculation sub-nodes, data processing sub-nodes, picture rendering sub-nodes and CALPOT calculation sub-nodes; for calculation of the CAPOST model;

the WRF calculation sub-node is used for completing CALRF model calculation; the task of the CALMET calculation sub-node is used for completing CALMET model calculation; the task of the CALPUFF calculation child node is used for completing CALPUFF model calculation; the task of the data processing child node is used for analyzing and storing data of the CALPUFF model calculation result; the task of the picture rendering child node is mainly to perform picture rendering on the data of the CALPUFF result; the CALPUT calculation sub-nodes are used for calculating the CALPUT model.

The task scheduling management system is responsible for scheduling and managing each task in the atmosphere model computing system; the task scheduling management system groups the tasks, realizes the operations of suspending, immediately executing, executing according to the time rule and resuming the execution, and is also used for checking the logs of the task execution.

In the present embodiment, the atmosphere model management system and the atmosphere model calculation system do not directly communicate with each other, but interact with each other through the model database and the cache server, as described with reference to fig. 2. The task scheduling system interacts with the atmosphere model computing system and is used for scheduling and distributing various computing tasks in the atmosphere model computing system. The atmosphere model management system needs to interact with the basic database and is mainly used for providing various basic data needed by case creation.

In this embodiment, the atmospheric model management system includes a model management module, a case management module, an emission reduction simulation module, a system management module, a tracing simulation module, and a computing service interface module;

the model management module is used for establishing and maintaining a model, the establishment of the model requires a user or a third-party system, and various parameters which can be defined by the model are set, wherein the various parameters comprise terrain parameters, pollutant parameters, model calculation parameters and picture rendering parameters; generating a base number configuration file, a terrain file and a working space of the model through the set model parameters; the user adds, deletes, modifies and queries the generated model;

the case management module is used for creating, calculating and maintaining cases and inquiring case results; the user or the third-party system creates a new model in the model management module and then creates a self-defined case under the model; the system divides cases with different calculation processes into different products, and a user selects different calculation model calculation processes to meet specific requirements;

most parameters in CALPUFF model calculation, such as pollution parameters, emission data and calculation time, can be mapped through self-defining parameters of the case;

after the case is created, the method is used for initiating the calculation of the case, inquiring the calculation state of the case and the time spent by each calculation link; after the case calculation is finished, the case calculation method is used for retrieving the calculation result of the case and various statistical analysis data based on the case calculation result;

finally, the user carries out maintenance operation on the case;

the emission reduction simulation module is used for customizing an emission reduction simulation plan by a user, selecting relevant information of emission reduction simulation, performing emission reduction on a certain parameter, performing emission reduction ratio, and performing emission reduction on a certain monitoring station;

a user sees the relevant information and state of the case in the emission reduction simulation list, and the emission reduction simulation result is graphically displayed;

the computing service interface module registers information issued by the computing platform in the service registration center, a user acquires configuration information of a relevant interface of an access platform by inquiring the information of the service registration center, and a third-party platform accesses services through multiple protocols;

the system management module is used for user and authority management, third-party system interface authority management, log management and the like.

The tracing simulation module can simulate the scene of unknown burst emission and calculate the possible existing range of the burst unknown emission source according to the simulation data and mathematical modeling.

In this embodiment, the task scheduling system includes a task management module, a task scheduling module, and a log management module;

in the task management module, a user establishes a plurality of task executors, and each task executor is responsible for executing all timing tasks below the executor; setting information of the name, the serial number, the IP and the port of the actuator by a user; a user views each actuator through a list page and modifies the parameters of the actuators;

a user establishes a task, sets a corresponding executor for the task to use, a routing strategy, an operation mode, task parameters, a responsible person and alarm mail information;

the task scheduling module is used for grouping the users by the executors, distributing a plurality of tasks to a certain executor and isolating the timing tasks of different services;

and the log management module is used for inquiring the log information by a user through the inquiry condition.

In a second embodiment, the present embodiment is described with reference to fig. 3 to 9, and the present embodiment is an example of the calculation method for implementing high performance of CALPUFF based on workflow in the first embodiment:

the embodiment is described with reference to fig. 3, the overall technical architecture of the atmosphere model computing service platform adopts the design modes of MVC and SOA, and the overall architecture can be divided into four layers, namely a base layer, a data layer, a support layer and an application layer.

MVC refers to some framework of MVC patterns that enforces the separation of application inputs, processing, and outputs. The use of an MVC application is divided into three core components: models, views and controllers. Each handling its own tasks. The relationship of the three layers is shown in fig. 4.

View layer view is the interface that a user sees and interacts with. For existing Web applications, views are interfaces composed of HTML elements, and in new Web applications, HTML still plays an important role in the views, but some new technologies have been developed, which include FLASH and some markup languages like XHTML, XML/XSL, WML, and Web services. The benefit of MVC is that it can handle many different views for an application. There is virtually no real processing that occurs in the view, whether the data is stored online or a list of employees, as the view is simply the way the data is output and the user is allowed to manipulate.

The model layer (model) represents enterprise data and business rules. Of the three components of MVC, the model has the most processing tasks. For example, it is possible to process a database with component objects such as EJBs and ColdFusion Components, and the data returned by the model is neutral, i.e., the model is independent of the data format, so that a model can provide data for multiple views, reducing code repeatability since the code applied to the model can be reused by multiple views only by writing it once.

The control layer (controller + service) accepts the user's input and calls the model and view to fulfill the user's requirements, so when clicking on a hyperlink in a Web page and sending an HTML form, the controller itself does not output anything and do any processing. It simply receives the request and decides which model component to invoke to process the request and then determines which view to use to display the returned data.

A Service Oriented Architecture (SOA) is a component model that splits different functional units of an application (called services) and ties them up through well-defined interfaces and contracts between these services. The interface is defined in a neutral manner and should be independent of the hardware platform, operating system and programming language in which the service is implemented. This allows services built into a wide variety of systems to interact in a uniform and versatile manner.

The system comprises a service-oriented architecture, a network and a server, wherein the service-oriented architecture can be used for carrying out distributed deployment, combination and use on loosely-coupled coarse-grained application components through the network according to requirements. The service layer is the basis of the SOA and can be directly called by the application, so that the artificial dependency of interaction with the software agent in the system is effectively controlled.

The SOA is a coarse-grained and loosely-coupled service architecture, and services communicate with each other through a simple and accurately defined interface without involving a bottom programming interface and a communication model. SOA can be viewed as a natural extension behind the B/S model, XML/Web Service technology.

SOA will help software engineers to stand a new high understanding of the development and deployment of various components in an enterprise-level architecture, which will help enterprise system architects to more quickly, reliably, and reusably structure the entire business system. Compared with the prior art, the system based on the SOA can more leisurely face the rapid change of the business.

The foundation layer construction is a foundation guarantee constructed by projects, and in the embodiment, the foundation layer mainly refers to various software environments on which the atmospheric model computing platform operates. Such as operating systems, databases, modeling software, application server software, and various other software. The respective software portions of the base layer will be described below.

In this embodiment, the atmosphere model computing platform needs to be run on a Linux operating system. The main reason is that the operation of the Linux system is very stable, the calculation efficiency is high, and the safety is high. The core of the system adopts a modular design, so that the system is easy to be simplified and transplanted on different hardware platforms, and has stronger scalability. The full network function is enriched, and the communication and network functions of the system are better than those of other operating systems. Very good portability, which refers to porting an operating system from one platform to a different platform and still enabling it to run properly.

In this embodiment, the wrf (weather Research forecast) model is a new generation of mesoscale forecast model and assimilation system, which are developed and researched by many scientists in the united states Research departments and universities. The WRF mode is a completely compressible non-static mode, adopts an Arakawa C grid, combines an advanced numerical method and a data assimilation technology, adopts various improved physical process parameter schemes, has the capability of multiple nesting and easy positioning at different geographic positions, integrates numerical weather forecast, atmospheric simulation and data assimilation, and can better improve simulation and forecast of mesoscale weather from meters to thousands of kilometers.

The main characteristics are as follows:

(1) the static mode is added with a non-static item on the basis of the MM5 mode;

(2) adopting an Arakawa C grid for WRF-ARW in the horizontal direction;

(3) selecting a following terrain coordinate system as a vertical coordinate, and taking the average sea level air pressure as a reference surface;

(4) there are a number of physical parameterization schemes, including: a micro-physical process scheme, a cloud convection parameterization scheme, a land process scheme, a boundary layer scheme, an atmospheric radiation scheme and the like;

(5) the rationale follows various laws such as newton's second law, mass and energy conservation equations, and gas testing laws;

(6) a plurality of physical options;

(7) unidirectional and bidirectional nesting;

WRF mode kinetic framework:

(1) fast wave horizontal propagation: pre-difference-post-difference scheme;

(2) vertical propagation of sound waves: an implicit scheme;

(3) horizontal direction: Adams-Bashforth protocol;

(4) vertical direction: the Crank-Nicholson protocol;

(5) turbulent Kinetic Energy (TKE), water in each phase, explicit, iterative, flux-corrected (called once every two time steps);

t, U, V (space) advection:

(1) horizontal direction: conservation of energy and vorticity quasi-energy, secondary conservation (conservation of square), second order;

(2) vertical direction: second conservation, second order;

(3) turbulent Kinetic Energy (TKE), water in each phase: reversible, flux corrected, positive fixed, conservative.

The present embodiment is described with reference to fig. 5, fig. 5 shows an operation mode of the WRF model, and the WRF Preprocessing System (WPS) is a module composed of three programs, and the three programs are used for preparing an input field for real data simulation. The respective uses of the three programs are: GEOGRID determines a mode area (including latitude and longitude ranges of the area, coordinates of a central point, grid nesting, grid points in the horizontal direction and resolution), and interpolates terrain data (including terrain elevation, land utilization types, vegetation coverage, soil types and the like) of static terrain data into grid points; extracting a meteorological element field from GRIB-format data by UNGRIB; METGRID is the horizontal interpolation of the extracted meteorological element field onto the grid points determined by georgid.

The WRF-ARW/NMM system is a core module of a WRF mode, has the advantages of high efficiency, easiness in mastering, capability of parallel operation and the like, data needs to be processed by a real program before the WRF program is operated, meteorological element data interpolated by a METGRID program is identified by the real program of the WRF mode, and the meteorological element data are vertically interpolated to an eta layer of the WRF mode to generate a required boundary layer file and initialize boundary conditions.

The present embodiment is described with reference to fig. 6 and 7, in the present embodiment, the WRF mode operation settings include basic data input, control file settings, and physical parameter settings, where the basic data includes data such as terrain and land utilization type, weather forecast grid point data GFS, historical weather reanalysis data FNL, and ground and high altitude observation data. The simulation settings comprise basic simulation settings such as nested grid range, map projection, simulation time and the like, the WRF mode provides various physical parameterization schemes, and a proper scheme is selected, so that simulation and forecast of mesoscale weather can be better improved.

Taking WRF simulation in the Chachen stage city in Hebei as an example, three layers of nested grids are created, an outer layer of grids provides a boundary field for an inner layer of grids and provides higher-precision gas image field data for a region, the grid resolution is 36km, 12km and 4km, a first layer of grids covers the national range, a second layer of grids covers most of the region of the east, a third layer of grids covers most of the region of Jingjin Ji and the surrounding region, 23 layers of grids are arranged in the vertical direction, and the top layer air pressure is 50 kpa. The pattern map adopts Lambert map projection, and the projection pattern is suitable for the medium latitude areas.

In this embodiment, the CALMET model is the diagnostic wind farm computing model developed by Sigma Research Corporation (now a subsidiary of Earth Tech, Inc) recommended by the U.S. EPA. CALMET is a meteorological module for describing an hour wind field and a temperature field in a three-dimensional grid simulation domain by using a mass conservation continuous equation, the core part of the CALMET comprises a diagnosis wind field and a microclimate field mode, the diagnosis wind field module carries out geomorphology dynamics, slope flow and terrain blocking effect adjustment on an initial guess wind field (a mesoscale mode output meteorological field, conventionally monitored ground and high-altitude meteorological data), a first-step wind field is generated, observation data are led in, and a final wind field is generated through interpolation, smoothing processing, vertical speed calculation, divergence minimization and the like. The CALMET module considers the dynamic influence of the terrain, the inclined airflow and the blocking effect in detail in the three-dimensional wind field simulation process.

Referring to fig. 8, in the present embodiment, the CALMET model is modeled by creating a model, for example, from chen stage city, north and river, and simulating grids covering the range of the chen stage city, with a grid resolution of 1km × 1km, a grid range of 200km in the east-west direction and 150km in the north-south direction, 10 vertical layers, 11 height planes, 0m to 3000m from the ground, 0m, 20m, 40m, 80m, 160m, 320m, 640m, 1000m, 1500m, 2200m, and 3000m, and a time resolution of 1 hour.

And taking a WRF mesoscale mode simulation result as initial gas field input of a CALMET mode, extracting partial grid gas field data, converting the extracted partial grid gas field data into a CALMET input requirement format file, and covering a CALMET grid area (namely covering a chennel city area) by the extracted grid range.

In this embodiment, the CALPUFF model is an air quality diffusion model developed by Sigma Research Corporation (now a subsidiary of Earth Tech, Inc.) recommended by the united states environmental Protection agency epa (environmental Protection agency) and is composed of three parts, namely, a CALMET meteorological module, a CALPUFF plume diffusion module, and a CALPOST-processing module, and is a multi-layer, multi-species pollution (such as SO) for simulating an unstable state₂、NO_xEtc.) taking into account the migration diffusion process, the dry-wet sedimentation process and the basic chemical transformation process of different pollutants under the weather conditions of time and space variation. The influence of complex terrains, water transport, boundary influence of coasts and sinking influence of buildings are considered, pollutants discharged from a source are simulated in an advection diffusion mode, and the concentration and the settling amount at a preset point can be estimated.

The CALPUFF plume diffusion mode has the following characteristics:

a) time-varying point source and surface source pollution can be treated;

b) regions of tens of meters to hundreds of kilometers can be simulated;

c) the pollutant concentration of one hour to one year can be predicted;

d) the simulation pollutant has more types;

e) the linear removal process and the chemical conversion mechanism of the pollutants are considered, and the secondary generation of the pollutants can be simulated, so that the method is suitable for simulation under the conditions of rough and complex terrain.

Referring to fig. 9, the CALPUFF mode calculation is mainly divided into two parts: the system comprises a CALMET meteorological processing module and a CALPUFF tobacco mass diffusion module, wherein the CALMET module is used for generating a meteorological field file required by a CALPUFF main module. The CALPUFF module is the main module of the mode and is a module for the modeA multi-pollutant and multi-layer Gaussian (Gaussian) diffusion model under unsteady and unsteady conditions is simulated or predicted. The concentration diffusion calculation can be carried out through the module, but the relevant data of the pollution emission source is required to be input externally, SO that the pollutants (such as SO) under the meteorological factors changing along with time and space positions can be obtained₂NOx, etc.).

Taking the chen platform city in the north of river as an example, a mode model is created, a simulation grid covers the range of the chen platform city, the grid resolution rate is 1km x 1km, the grid range is 200km in the east-west direction and 150km in the north-south direction, the number of vertical layers is 10, 11 height surfaces is 0m, 20m, 40m, 80m, 160m, 320m, 640m, 1000m, 1500m, 2200m and 3000m from the ground surface to 3000 m.

And (3) taking the CALMET result as an input condition, and performing simulation calculation on air quality by combining with the emission list data of pollution sources in a Schchen platform market to customize an air quality model. The simulation takes into account wet-dry settling of the contaminants, including gas dry settling and particulate dry settling.

In this embodiment, the CALPUFF pollution source is, for example, simulated from chenchen stage city in north of the river, and the pollution source data adopts a 2017 pollutant emission list from chen stage city, where the pollutants include sulfur dioxide, nitrogen oxides, particulate matter PM10, fine particulate matter PM2.5, VOCs, carbon monoxide, and the like, and the emission list calculates main industry pollution emissions including fossil fuel fixed combustion sources, process sources, mobile sources, solvent use sources, agricultural sources, dust emission sources, and the like. The fixed fossil fuel combustion source and the technological process source cover key industrial industries in a chenchen table market, including electric power heating, industrial boilers, civil boilers, steel, cement, chemical fiber, glass, coking and the like. The mobile source is divided into a road mobile source and a non-road mobile source, and the dust source is divided into soil dust, road dust, construction dust and storage yard dust.

Because the number of industrial enterprises in the chenchen platform market is more, the enterprise is mainly discharged as the point source input mode to the pollutant is selected in this simulation, and about 360 point sources total, mainly discharge pollutant sulfur dioxide, nitrogen oxide, particulate matter, carbon monoxide. Dividing the mobile sources and the dust sources into small grid surface source input modes through a geographic program for calculation, wherein the number of the surface sources is about 4000, the mobile sources mainly discharge nitrogen oxides, and the dust sources are the main sources of particulate matters PM10 and PM 2.5. The CALPUFF mode simulates pollution diffusion concentrations of sulfur dioxide, nitrogen oxides, particulate matter PM10, fine particulate matter PM2.5 and carbon monoxide.

In this embodiment, the model data database in the base layer is a PostgreSQL database, which is an object-relational database management system of free software with very complete characteristics, and is an object-relational database management system based on post tress, version 4.2, developed by a computer system in california university. Many of the leading concepts of POSTGRES appear in commercial web site databases only relatively late. PostgreSQL supports most SQL standards and offers many other modern features such as complex queries, foreign keys, triggers, views, transaction integrity, multi-version concurrency control, etc. Also, PostgreSQL can be extended in many ways, such as by adding new data types, functions, operators, aggregate functions, prime methods, procedural languages, etc.

The main advantages of PostgreSQL are as follows:

(1) the operating system supports WINDOWS, Linux, UNIX, MAC OS X, BSD.

(2) From the basic function, ACID, association integrity, database transaction, Unicode multinational language are supported.

(3) Table and View aspects, PostgreSQL supports temporary tables, while materialized views may be simulated using stored procedures and triggers in PL/pgSQL, PL/Perl, PL/Python, or other procedural languages.

(4) In the aspect of indexing, R-/R + tree indexes, hash indexes, reverse prime indexes, partial prime indexes, Expression indexes, GiST and GIN (used for accelerating full-text retrieval) are comprehensively supported, and bitmap indexes are supported from 8.3 versions.

(5) On other objects, support data fields, support storage procedures, triggers, functions, external calls, cursors 7) data table partitioning aspects, support 4 partitions, namely ranges, hashes, mixes, lists.

(6) The support of the transaction is tested more thoroughly than MySQL in terms of the support of the transaction.

(7) In the aspect of MyISAM table processing mode, MySQL adopts table locking for a transactionless MyISAM table, 1 query running for a long time is likely to block the table updating, and PostgreSQL does not have the problem.

(8) From the storage process perspective, PostgreSQL supports the storage process, but currently MySQL does not. This advantage is apparent because the presence of stored procedures also avoids the transmission of large amounts of raw SQL statements over a network.

(9) And in the aspect of sub-query support, MySQL does not support sub-queries.

(10) The extension aspect of the user-defined function, PostgreSQL, can be extended more conveniently using UDF (user-defined function).

Nginx (enginex) in the base layer is a high-performance HTTP and reverse proxy web server, and also provides IMAP/POP3/SMTP services. The method is characterized by less occupied memory and strong concurrency capability, and the actual concurrency capability of nginx is better in the same type of web servers, and users using the nginx website have the following characteristics: baidu, Jingdong, Xinlang, Neiyi, Tengchun, Taobao, etc. It has many very superior properties:

(1) nginx can be compiled to run on most Unix, Linux OSs, and has windows ported versions.

(2) In case of high concurrency of connections, Nginx is a good substitute for Apache service: nginx is one of the software platforms often chosen by the boss for virtual host business in the United states. Responses up to a number of 50,000 concurrent connections can be supported.

(3) Nginx as a load balancing service: the Nginx can directly support the Rails and the PHP program to carry out external service inside, and can also support the external service as HTTP proxy service. Nginx is written by C, and the system resource overhead and the CPU use efficiency are much better than those of Perlbal.

(4) Processing static files, indexing files and automatic indexing; the file descriptor buffer is opened.

(5) Non-cached reverse proxy acceleration, simple load balancing and fault tolerance.

(6) FastCGI, simple load balancing and fault tolerance.

(7) Modular construction. Including gzapping, byte ranks, chunked responses, and filters such as SSI-filter. If multiple SSIs present in a single page are processed by FastCG or other proxy servers, this process can run in parallel without waiting for each other.

Tomcat in the base layer is a core item in the Jakarta project of the Apache Software Foundation (Apache Software Foundation), and is commonly developed by Apache, Sun and other companies and individuals. With the participation and support of Sun, the latest Servlet and JSP specifications can always be embodied in Tomcat, and Tomcat 5 supports the latest Servlet 2.4 and JSP 2.0 specifications. Because Tomcat technology is advanced, stable in performance and free of charge, the Tomcat technology is popular with Java enthusiasts and accepted by some software developers, and becomes a popular Web application server at present.

The Tomcat server is a free Web application server with open source codes, belongs to a lightweight application server, is commonly used in small and medium-sized systems and occasions where concurrent access users are not many, and is the first choice for developing and debugging JSP programs. For a novice, it is believed that when an Apache server is configured on a machine, it can be used to respond to access requests for HTML (an application in the standard universal markup language) pages. Tomcat is in fact an extension of the Apache server, but it runs independently at runtime, so when you run Tomcat, it actually runs separately as a process independent of Apache.

The recipe is that Apache services HTML pages when configured correctly, while Tomcat actually runs JSP pages and servlets. In addition, the Tomcat has the function of processing HTML pages like Web servers such as IIS, and is also a Servlet and JSP container, and the independent Servlet container is the default mode of Tomcat. However, Tomcat is not as capable of handling static HTML as Apache servers. The current latest version of Tomcat is 9.0.

The redis in the base layer is a key-value storage system. Similar to Memcached, it supports relatively more stored value types, including string, list, set, zset, and hash. These data types all support push/pop, add/remove, and intersect union and difference, and richer operations, and these operations are all atomic. On this basis, redis supports various different ways of ordering. Like memcached, data is cached in memory to ensure efficiency. The difference is that the redis can periodically write updated data into a disk or write modification operation into an additional recording file, and master-slave synchronization is realized on the basis of the updated data or the modification operation.

Redis is a high-performance key-value database. The occurrence of redis greatly compensates the shortage of key/value storage such as memcached, and can play a good role in supplementing the relational database in some occasions. The method provides clients such as Java, C/C + +, C #, PHP, JavaScript, Perl, Object-C, Python, Ruby, Erlang and the like, and is convenient to use.

Redis supports master-slave synchronization. Data may be synchronized from a master server to any number of slave servers, which may be master servers associated with other slave servers. This enables Redis to perform single-level tree replication. The storage disk can write data intentionally or unintentionally. Due to the fact that the publish/subscribe mechanism is completely achieved, when the slave database synchronizes the tree anywhere, the slave database can subscribe to one channel and receive the complete message publishing record of the master server. Synchronization is helpful for scalability of read operations and data redundancy.

The ZooKeeper in the base layer is a distributed and open-source distributed application program coordination service, is an open-source implementation of Chubby of Google, and is an important component of Hadoop and Hbase. It is a software that provides a consistent service for distributed applications, and the functions provided include: configuration maintenance, domain name service, distributed synchronization, group service, etc. The ZooKeeper aims to package complex and error-prone key services and provide a simple and easy-to-use interface and a system with high performance and stable functions for users.

In this embodiment, global prediction GFS data of the nme (national Centers for Environmental prediction) is used as the initial boundary condition for prediction in the future weather prediction mode, the prediction data can predict the weather 8 days in the future for 192 hours, the spatial resolution is 0.5 ° × 0.5 °, the prediction is updated every 6 hours at 3h intervals, the prediction is started four times a day (00UTC, 06UTC, 12UTC, and 18UTC), and the data content includes parameters such as air pressure, temperature, wind speed, humidity, and the like in different height layers. The system downloads the latest weather forecast data at regular time every day through the automatic script, and automatically starts a WRF mode to forecast future weather in Lanzhou city.

In this embodiment, the WRF model data in the data layer includes meteorological data and basic geographic data; the meteorological data is used for simulating historical weather and adopts weather reanalysis data FNL data, the global grid point data is jointly manufactured and issued on an NCEP official network by an American national environmental prediction center (NCEP) and an American national atmospheric research center (NCAR), the most advanced global data assimilation system and a perfect database are adopted to carry out quality control and assimilation processing on observation data of various data sources (ground, ships, radio exploration, satellites and the like), so that a set of complete reanalysis data is obtained, and the method has the characteristics of time diversity, high density, strong continuity, higher resolution, rich content and the like, and can effectively make up the defects of conventional observation data in the aspect of disaster weather analysis.

The FNL data has a spatial resolution of 1 DEG x 1 DEG and a temporal resolution of 6 hours. Global data analysis was performed at four times, 0, 6, 12, and 18, of world time each day. The data content includes air pressure, temperature, relative humidity, rainfall and the like. The data format is divided into two types of GRIB1 and GRIB2, wherein GRIB1 data time is from 1999.07.30 to 2007.12.06, GRIB2 data time is from 2007.12.06 to the present, and the data format is continuously updated. The meteorological data format provided this time is GRIB 2.

The base geographic data includes: terrain elevation, land use type, other underlying surface information, etc. Terrain data GTOP030 with a resolution of 30 ", land use type data USGS, MODIS, USGS containing 24 types of land types, MODIS containing 20 land types, satellite land cover product material, resolution up to 30". Other underlying data includes vegetation type, soil moisture, soil texture, etc.

The point source emission data comprises the source position, effective height, altitude, emission temperature, emission rate, unit, emission period and the like of each point. Dat is a point source parameter file, including point source emission data, as well as detailed, randomly varying emission parameters. In a point source file, the chimney parameters and the discharge rate can be specified in a file, and the plume lifting height needs to be simulated and calculated through a calculation formula in a CALPUFF model.

The point source file includes a series of time invariant quantities and time variant quantities, the time invariant quantities being: chimney height, diameter, coordinates, building wash down sign, user defined code, building height and width. The horizontal and vertical systems are independent of each other in the model, the horizontal coordinates are specified in terms of the coordinates of the meteorological grid, and the vertical layer accepts the source information from the calculation of plume rise inside the model. The amounts that change over time are: outlet temperature, outlet velocity, and discharge rate of various pollutants.

The point source parameters need to be obtained by processing an area pollutant discharge list, and the information to be read includes:

name, number of sources, type of pollutant emitted, UTM zone, start date, start time, end date, end time, version of CALPUFF used, and analog range flag.

Chemical equation and molecular weight of the contaminant.

A. Amount not changing with time: coordinates; the geometric height and diameter of the chimney; altitude, building wash down markers and custom markers. Where in representing a building wash mark,

0-means that building downwash is not considered, and 1-means that building downwash is considered.

B. Amount changing with time: outlet temperature, outlet velocity, and discharge rate of various pollutants.

Dat area source parameter file includes area source position, effective height, altitude, initial diffusion coefficient, unit and discharge period, including detailed area source discharge data, and discharge parameters that vary at will. In the non-point source file, the value of the source emission parameter and the emission rate can be specified in each step of operation, and the smoke plume lifting height of each source needs to be simulated and calculated through a calculation formula in a CALPUFF model.

Dat file is an ASCII data file containing headers and data blocks. There are data blocks that vary with time and also do not vary with time.

The monitoring station data includes: site coordinates, name, type are monitored. The air quality monitoring site data includes hourly daily monitoring values for conventional contaminants including: PM10, PM2.5, SO2, NO2, O3, CO, the types of monitored values include: hour monitor values, 24 hour average values for each contaminant, ozone includes 8 hour slip values, 24 hour slip values. And according to the air quality index AQI calculated according to each pollutant monitoring value, according to the AQI value and the standard, the air quality is classified according to grades.

Discharging enterprise information data

The pollutant discharge enterprise information comprises:

the area to which the enterprise belongs; enterprise coordinates;

the altitude of the place;

GDP of enterprise year;

the industry belongs to;

discharging amount of pollutants;

discharge port information: discharge height, outlet temperature, outlet rate, etc.

In this embodiment, the frame of the supporting layer is specifically composed of the following parts:

spring Framework, Spring boot, Eventbus, Mybatis, Redisson, Net, Velocity, Dubbo, HikaricP, CXF, Proj4j, Work Flow, Ganymed, Json, and Caffeine;

the Spring Framework is a Framework of a Framework multi-layer j2ee system based on IOC and AOP, and allows a user to select one module to use according to the needs of the user.

The Spring Framework is a core Framework of the whole platform, and other various components are integrated into the Spring Framework in a modularized mode.

Spring Boot is a completely new framework provided by the Pivotal team, and is designed to simplify the initial building and development process of new Spring applications. The framework uses a specific way to configure, thereby eliminating the need for developers to define a templated configuration. In this way, Spring boots are dedicated to become a leader in the booming field of rapid application development.

The Spring Boot can enable the configuration management of the whole platform to become very quick, and has better flexibility and expansibility.

EventBus is an event handling mechanism of Guava, and is an elegant implementation of observer patterns (production/consumer programming models) in design patterns. EventBus is a very elegant and simple solution for the event listening and publish-subscribe modes.

The EventBus is responsible for the functions of sending, caching, routing and the like of the messages on the platform.

MyBatis is an excellent persistent layer framework that supports customized SQL, stored procedures, and high-level mapping. MyBatis avoids almost all JDBC code and manual setting of parameters and acquisition of result sets. MyBatis can use simple XML or annotations to configure and map native information, mapping interfaces and Java's POJOs (Plain Ordinary Java Object) into records in the database.

Mybatis is mainly responsible for ORM functions and data reading and mapping of a database in the platform.

Redisson is a Java resident memory data grid based on Redis. Redisson fully utilizes a series of advantages provided by a Redis key value database on a NIO-based Netty framework, and provides a series of common tool classes with distributed characteristics for users on the basis of common interfaces in a Java utility kit. The original tool kit serving as a coordinated single-machine multithreading concurrent program obtains the capability of coordinating a distributed multi-machine multithreading concurrent system, and the difficulty of designing and researching a large-scale distributed system is greatly reduced. Meanwhile, by combining various featured distributed services, the cooperation between programs in the distributed environment is further simplified.

Redisson is mainly responsible for connecting with a redis cache server, locking in a distributed mode and carrying out various atomic operations on cache objects in the redis cache server on the platform.

Netty is a java open source framework provided by JBOSS. Netty provides an asynchronous, event-driven web application framework and tools for the rapid development of high-performance, high-reliability web servers and client programs. That is, Netty is a NIO-based client, server-side programming framework that can be used to ensure that you can quickly and easily develop a web application, such as a client, server-side application that implements a protocol. Netty is equivalent to simplifying and streamlining the programming development process of web applications, such as: socket service development based on TCP and UDP. "quick" and "simple" do not create maintenance or performance problems. Netty is a well-designed project that has absorbed the experience of implementing a variety of protocols, including FTP, SMTP, HTTP, and various binary text protocols. Finally, Netty successfully finds a way to ensure the performance, stability and flexibility of the application while ensuring the easy development.

Netty is mainly responsible for linking, maintaining and operating functions of basic HTTP, FTP and TCP communication channels in the platform. The platform can communicate with other external systems through various protocols.

Velocity is a java based template engine (template engine). It allows anyone to reference the object defined by the java code using only a simple template language (template language). It can generate SQL and PostScript, XML from templates (templates), it can also be used as a stand-alone tool to generate source code and reports, or as an integrated component of other systems.

The Velocity is mainly responsible for managing templates of various model parameter files in the platform and dynamically generating various parameter files according to the model parameter templates when the system runs.

Dubbo is a high-performance excellent service framework sourced by the company Alibarba, so that the application can realize the output and input functions of the service through high-performance RPC and can be seamlessly integrated with the Spring framework. Dubbo is a high-performance, lightweight, open-source Java RPC framework that provides three core capabilities: interface-oriented remote method invocation, intelligent fault tolerance and load balancing, and automatic registration and discovery of services. The main characteristics are as follows:

(1) interface proxy oriented high performance RPC calls. The method provides high-performance remote invocation capability based on the proxy, and the service shields the bottom-level details of remote invocation for developers by taking the interface as granularity.

(2) And (4) intelligent load balancing. Various load balancing strategies are built in, the health condition of the downstream node is sensed intelligently, the call delay is reduced remarkably, and the system throughput is improved.

(3) Services are automatically registered and discovered. And various registration center services are supported, and the online and offline of service instances are sensed in real time.

(4) High scalability. Following the design principle of microkernel + plug-in, all core capabilities such as Protocol, Transport, and Serialization are designed as extension points, and the built-in implementation and the third-party implementation are treated equally.

(5) And scheduling the traffic in a running period. Routing strategies such as conditions and scripts are built in, and functions such as gray scale release and priority of the machine room are achieved easily by configuring different routing rules.

(6) Visual service management and operation and maintenance. And (3) providing rich service treatment and operation tools: and inquiring service metadata, service health state and calling statistics at any time, issuing a routing strategy in real time and adjusting configuration parameters.

The Dubbo frame is mainly responsible for publishing micro-services in the platform, and the services can be used by other subsystems in the platform or various third-party application systems on the Internet.

HiKaricP is a top-line of database connection pools, and is a very superior database connection pool component.

In the platform, the HiKaricP is mainly responsible for the functions of creating, managing, scheduling and the like of a database connection pool. Because each system in the platform needs to frequently interact with the database, the efficiency of connecting the system and the database can be greatly improved through the database connection pool.

Apache CXF is an open source Services framework, and CXF helps you build and develop Services like JAX-WS using fronted programming API. These Services may support a variety of protocols, such as: SOAP, XML/HTTP, RESTful HTTP, or CORBA, and can run over a variety of transport protocols, such as: HTTP, JMS or JBI, CXF greatly simplifies the creation of Services, while it inherits XFire tradition and can be seamlessly integrated with Spring naturally. XF contains a number of functional features, but is primarily focused on the following:

the Web Services standard is supported: CXF supports a variety of Web Services standards including SOAP, Basic Profile, WS-Addressing, WS-Policy, WS-ReliableMessaging, and WS-Security. Frontards: CXF supports multiple "fronted" programming models, CXF implements JAX-WS API (following JAX-WS version 2.0 TCK), it also contains a "simple fronted" that allows creation of clients and EndPoint without the need for Annotation. CXF supports both WSDL-first development and Java-derived code-first development modes. Easy to use: CXF is designed to be more intuitive and easy to use. A large number of simple APIs are used for quickly constructing Services with code priority, various Maven plug-ins enable integration to be easier, JAX-WS APIs are supported, a more simplified XML configuration mode of Spring 2.0 is supported, and the like. Binary and legacy protocols are supported: the design of CXF is a pluggable architecture, which can support both XML and non-XML type binding, such as: JSON and CORBA.

CXF is primarily responsible for providing the functionality of web services in this platform. Thus, the system can provide services for third-party systems developed in various languages on the internet.

Proj4j is the best known map projection library of open source GIS, and Proj4 is used directly or indirectly for projection by software such as GRASS GIS, MapServer, PostGIS, Thuban, OGDI, Mapnik, Topocad, GDAL/OGR, etc. The functions of the Proj4 mainly include conversion of longitude and latitude coordinates and geographic coordinates, conversion of a coordinate system including reference conversion, and the like, and the use of the conversion function of the longitude and latitude coordinates and the geographic coordinates is described below in a command line manner and a programming manner.

The Proj4j is mainly responsible for the conversion function of various coordinate systems and some operations related to GIS in the platform.

WorkFlow (WorkFlow) is a computational model of a WorkFlow, i.e., logic and rules that organize how the work in the WorkFlow is up and down are represented in a computer in an appropriate model and computed. The main problems to be solved by the workflow are: to achieve a business goal, automatic delivery is made between multiple participants, using computers, according to certain predefined rules. The workflow is part of a Computer Supported Collaborative Work (CSCW). The latter is a general study on how a group can achieve cooperative work with the help of computers.

In the present embodiment, the workflow used is modified based on a workflow engine of another open source, and the main function is to manage and execute the process of model calculation. Through management of the mode of the workflow, the responsible computing process can be modularized, so that the model computing process is flexible and changeable, and the method can be suitable for various computing scenes and computing requirements.

Ganymed SSH-2for Java is a packet that implements the SSH-2 protocol in pure Java. It can be used to connect SSH servers directly in Java programs.

In this embodiment, each system is deployed on a different linux server. In the course of system usage, there are many functions that rely on the operation of the SSH protocol. Ganymed is mainly responsible for completing these operations through SSH protocol, and assists the function of the system to be realized.

JSON (JavaScript Object Notation) is a lightweight data exchange format. It stores and represents data in a text format that is completely independent of the programming language, based on a subset of ECMAScript (js specification set by the european computer association). The compact and clear hierarchy makes JSON an ideal data exchange language. The network transmission method is easy to read and write by people, is easy to analyze and generate by machines, and effectively improves the network transmission efficiency.

In the embodiment, a Json mode is adopted for direct data interaction of a plurality of interfaces and systems, and different Json components are used in different application scenes according to the characteristics of all Json components. Such as Gson, fastJson, JackJson.

Caffeine is a high-performance Java cache component. Various caching strategies are supported, and the performance is superior.

In the embodiment, Caffeine is responsible for caching small-volume content, and can provide the operation efficiency of the system in many links by combining with redis.

In the embodiment, the application layer is integrated with model management, case calculation, meteorological data post-processing, source contribution rate analysis, emission reduction simulation, picture rendering management, projection coordinate conversion, model result statistical analysis, task scheduling and system management;

the model management may support computing services where multiple models are online at the same time. The user can manage the models and modify the parameters of the models on the platform.

Case management is used to create a variety of different computing cases that a user can manage and modify the parameters of and submit the calculations for the cases on the platform.

The case calculation can be performed for various cases, such as single case calculation, composite case calculation, and composite normalized case calculation.

After the case calculation for the atmospheric computing service platform is completed through the meteorological data post-processing, various result files such as CON files and LST files can be generated. The platform needs to parse the file and process and generate a data file needed by the subsequent link.

The source contribution rate analysis is used for the atmosphere calculation service platform to calculate through a composite case or a composite normalized case, and can statistically analyze each parameter of each pollution source in the case aiming at the contribution rate with a certain monitoring station.

The emission reduction simulation is used for the atmosphere computing service platform to generate different emission reduction plans according to the specified emission reduction target. For example, the emission reduction can be carried out in an equal proportion, and the emission reduction can also be optimized by GDP.

The picture rendering management is used for rendering the calculation result into various pictures by the platform after the case calculation of the atmosphere calculation service platform is finished. The user can preset various rendering schemes of the pictures so as to refer to the schemes for picture rendering in the calculation process.

The projection coordinate transformation is used for supporting the projection coordinate transformation of various projection coordinate systems on the atmospheric computing service platform. Through projection coordinate conversion, data of different projection coordinate systems can be standardized and used uniformly.

The statistical analysis of model results may perform various statistical analyses on the data of the calculation results.

Task scheduling is used to provide the functions of scheduling the execution, management, and log viewing of the upper compute tasks of the various compute nodes.

The system management mainly comprises user and authority management, third-party system interface authority management, log management and the like.

The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (5)

1. The calculation method for realizing the CALPUFF high performance based on the workflow is characterized by comprising the following steps: the method is realized based on an atmosphere model calculation service system, wherein the atmosphere model calculation service system comprises an atmosphere model management system, an atmosphere model calculation system and a task scheduling management system;

the third-party system interacts with the whole atmosphere model calculation service system through the atmosphere model management system;

the atmosphere model management system is used for facing the use of a user and interacting with a third-party system; the system is used for realizing the functions of management of a calculation model, case management, statistical analysis of model data, management of a calculation service interface and system management;

the atmosphere model calculation system is responsible for completing the calculation process of the model and the subsequent processing of the calculation result; the method specifically comprises WRF model calculation, CALMET model calculation, CALPUFF model calculation and CALPUT model calculation;

the atmosphere model computing system divides a computing task into a plurality of task nodes according to the mode of the workflow and completes the task nodes one by one; the task nodes comprise WRF calculating sub-nodes, CALMET calculating sub-nodes, CALPUFF calculating sub-nodes, data processing sub-nodes and picture rendering sub-nodes;

the WRF calculation sub-node is used for completing CALRF model calculation; the task of the CALMET calculation sub-node is used for completing CALMET model calculation; the task of the CALPUFF calculation sub-node is used for completing CALPUFF model calculation; the task of the data processing child node is used for analyzing and storing data of the CALPUFF model calculation result; the task of the picture rendering child node is mainly to perform picture rendering on data of a calpuff result;

the task scheduling management system is responsible for scheduling and managing each task in the atmosphere model computing system; the task scheduling management system groups the tasks, realizes the operations of suspending, immediately executing, executing according to the time rule and resuming the execution, and is also used for checking the logs of the task execution.

2. The method for calculating CALPUFF high performance based on workflow of claim 1, wherein: the atmosphere model management system is not directly communicated with the atmosphere model calculation system, but is interacted with the atmosphere model calculation system through a model database and a cache server; the task scheduling system interacts with the atmosphere model computing system and is used for scheduling and distributing various computing tasks in the atmosphere model computing system; the atmosphere model management system interacts with the basic database and is used for providing various basic data required by case creation.

3. The method for calculating CALPUFF high performance based on workflow of claim 1, wherein: the atmosphere model computing system is also used for carrying out subsequent processing on the model result; including picture rendering and contribution rate analysis.

4. The method for calculating CALPUFF high performance based on workflow of claim 1, wherein: the atmosphere model management system comprises a model management module, a case management module, an emission reduction simulation module, a system management module and a calculation service interface module;

the model management module is used for establishing and maintaining a model, the establishment of the model requires a user or a third-party system, and various parameters which can be defined by the model are set, wherein the various parameters comprise terrain parameters, pollutant parameters, model calculation parameters and picture rendering parameters; generating a cardinal number configuration file, a terrain file and a working space of the model through the set model parameters; the user adds, deletes, modifies and queries the generated model;

the case management module is used for creating, calculating and maintaining cases and inquiring case results; the user or the third-party system creates a new model in the model management module and then creates a self-defined case under the model; the system divides cases with different calculation processes into different products, and a user selects different calculation model calculation processes to meet specific requirements;

most parameters in CALPUFF model calculation, such as pollution parameters, emission data and calculation time, can be mapped through self-defining parameters of the case;

after the case is created, the method is used for initiating the calculation of the case, inquiring the calculation state of the case and the time spent by each calculation link; after the case calculation is finished, the case calculation method is used for retrieving the calculation result of the case and various statistical analysis data based on the case calculation result;

finally, the user carries out maintenance operation on the case;

the emission reduction simulation module is used for customizing an emission reduction simulation plan by a user, selecting relevant information of emission reduction simulation, performing emission reduction on a certain parameter, performing emission reduction ratio, and performing emission reduction on a certain monitoring station;

a user sees the relevant information and state of the case in the emission reduction simulation list, and the emission reduction simulation result is graphically displayed;

the computing service interface module registers information issued by the computing platform in the service registration center, a user acquires configuration information of a relevant interface of an access platform by inquiring the information of the service registration center, and a third-party platform accesses services through multiple protocols;

the system management module is used for user and authority management, third-party system interface authority management and log management.

5. The method for calculating CALPUFF high performance based on workflow of claim 1, wherein: the task scheduling system comprises a task management module, a task scheduling module and a log management module;

in the task management module, a user establishes a plurality of task executors, and each task executor is responsible for executing all timing tasks below the executor; setting information of an actuator name, a serial number, an IP (Internet protocol) and a port by a user; a user views each actuator through a list page and modifies the parameters of the actuators;

a user establishes a task, sets a corresponding executor for the task, and sets a routing strategy, an operation mode, task parameters, a responsible person and alarm mail information;

the task scheduling module is used for grouping the users by the actuators, distributing a plurality of tasks to a certain actuator and isolating the timing tasks of different services;

and the log management module is used for inquiring the log information by a user through the inquiry condition.

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